DOCSLIB.ORG

Introduction to the Eddy Covariance Method

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Introduction to the Eddy Covariance Method INTRODUCTION TO THE EDDY COVARIANCE METHOD GENERAL GUIDELINES, AND CONVENTIONAL WORKFLOW This introduction has been created to familiarize a beginner with general theoretical principles, requirements, applications, and processing stepsG. ofBurba the Eddy Covarianceand D. method. Anderson It is intended to assist readers in the further understanding of the method and references such as textbooks, network guidelines and journal papers. It is also intended to help students and researchers in the field deployment of the Eddy Covariance method, and to promote its use beyond micrometeorology. The notes section at the bottom of each slide can be expanded by clicking on the ‘notes’ button located in the bottom of the frame. This section contains text and informal notes along with additional details. Nearly every slide contains references to other web and literature references, additional explanations, and/or examples. Please feel free to send us your suggestions. We intend to keep the content of this work dynamic and current, and we will be happy to incorporate any additional information and literature references. Please address mail to george.burba at licor.com with the subject “EC Guidelines”. LI-COR Biosciences 1 CONTENT Introduction_______________________3 - effect of canopy roughness Introduction - effect of stability II.4 Quality control of Eddy Covariance data 102 Purpose - summary of footprint QC general Acknowledgements Testing data collection QC nighttime Layout Testing data retrieval Validation of flux data Keeping up maintenance Filling-in the data I. Eddy Covariance Theory Overview_7 Experiment implementation summary Storage Flux measurements in general Integration State of Eddy Covariance methodology II. 3 Data processing and analysis __ 73 Air flow in ecosystem Unit conversion II.5 Eddy covariance workflow summary ___111 How to measure flux Despiking Basic derivations Calibration coefficients III. Alternative methods_____ _____113 Practical formulas Coordinate rotation Eddy Accumulation Major assumptions Time delay Relaxed Eddy Accumulation Major sources of errors De-trending Bowen Ratio Energy Balance Error treatment Aerodynamic method Use in non-traditional terrains Applying corrections___________________81 Resistance approach Eddy Covariance theory summary - frequency response corrections Chamber measurements -co-spectra Other alternative methods II. Eddy Covariance Workflow _22 - transfer functions - applying frequency response corrections IV. Future development_____ _____ 121 II.1 Experimental design _____________24 - time response Future of Eddy Covariance method Purpose and variables - sensor separation -expansion Instrument requirements - tube attenuation -airborne Eddy Covariance instrumentation (25-55) - digital sampling - difficult terrains Data collection and processing software - path and volume averaging LIDAR/RADAR Location requirements - hi-pass filtering Laser spectroscopy Maintenance plan - low-pass filtering Space Summary of experimental design - sensor response mismatch Multiplexing/Networking - total transfer function II.2 Experiment implementation ______ 51 - frequency response summary V. Eddy Covariance Review Summary 129 Tower placement Choosing time average VI. Useful resources_____ ____ _131 Sensor height and sampling frequency Webb-Pearman-Leuning correction VII. References and future readings __ 134 Footprint Sonic correction - visualizing the concept Other corrections - effect of measurement height Summary of corrections Burba & Anderson SlideSlide 2 2 ©LI-CORBurbaBiosciences & Anderson ThisYou introduction can expand thehas indexbeen bycreate clickingd to familiarizeon the ‘Outline’ a begi buttonnner within the general lower lefttheoretical of the frame. principles, This requirements,index is linked applications, to the contents and profocessing each slide steps and of can the be Eddy used Covari for quickance and method. easy navigationIt is intended of theto assist readers in further understanding of the method and references such as textbooks, network guidelinescontents of and these journal guidelines. papers. It is are also intended to help students and researchers in the field deployment of the Eddy Covariance method, and to promote its use beyond micrometeorology. The notes section at the bottom of each slide can be expanded by clicking on the ‘notes’ button located in the bottom of the frame. This section contains text and informal notes along with additional details. Nearly every slide contains references to other web and literature references, additional explanations, and/or examples). Please feel free to send us your suggestions. We intend to keep the content of this work dynamic and current, and we will be happy to incorporate any additional information and literature references. Please address mail to [email protected] with subject “EC Guidelines”. the question mark icon and blue font color indicate scientific references, web-links and ? other information sources covering related to the topic of the slide the exclamation point icon and red font color indicate warnings and describe potential ! pitfalls related to the topic of the slide 2 INTRODUCTION • The Eddy Covariance method is a very useful technique to measure and calculate turbulent fluxes within the atmospheric boundary layer • Modern instruments and software can potentially expand the use of the method beyond micrometeorology to a widely-used tool for biologists, ecologists, entomologists, etc. • Main challenge of the method for a non-expert is the shear complexity of system design, implementation and processing the large volume of data Burba & Anderson SlideSlide 3 3 ©LI-CORBurbaBiosciences & Anderson Below are few examples of the sources of information on the various methods of flux measurements, specifically the Eddy Covariance method: ? Rosenberg, N.J., B.L. Blad & S.B. Verma. 1983. Microclimate. The biological environment. A Wiley-interscience publication. New York. 255-257 Baldocchi, D.D., B.B. Hicks and T.P. Meyers. 1988. 'Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods', Ecology, 69, 1331-1340 Verma, S.B., 1990. Micrometeorological methods for measuring surface fluxes of mass and energy. Remote Sensing Reviews, 5: 99-115. Wesely, M.L., D.H. Lenschow and O.T. 1989. Flux measurement techniques. In: Global Tropospheric Chemistry, Chemical Fluxes in the Global Atmosphere. NCAR Report. Eds. DH Lenschow and BB Hicks. pp 31-46 3 PURPOSE • Help a non-expert in gaining a basic understanding of the Eddy Covariance method and point out valuable references • Provide explanations in a simplified manner first, and then to elaborate with specific details • Promote a further understanding of the method via more advanced sources (textbooks, papers) • Help design experiments for the specific needs of a new Eddy Covariance user Burba & Anderson SlideSlide 4 4 ©LI-CORBurbaBiosciences & Anderson Here we try to help a non-expert to understand the general principles, requirements, applications, and processing steps of the Eddy Covariance method. Explanations are given in a simplified manner first, and then, elaborated with some specific examples; alternatives to the traditionally used approaches are also mentioned. The basic information presented here is intended to provide a foundational understanding of the Eddy Covariance method, and to help new Eddy Covariance users design experiments for their specific needs. A deeper understanding of the method can be obtained via more advanced sources, such as textbooks, network guidelines and journal papers. The specific applications of the Eddy Covariance method are numerous, and may require specific mathematical approaches and processing workflows. This is why there is no one single recipe, and it is important to study further, all aspects of the method in relation to a specific measurement site and a specific scientific purpose. 4 ACKNOWLEDGMENTS We would like to acknowledge a number of scientists who have contributed to this review directly via valuable advice and indirectly via scientific papers, textbooks, data sets, and personal communications Particularly we thank Drs. Dennis Baldocchi, Dave Billesbach, Robert Clement, Tanvir Demetriades-Shah, Thomas Foken, Beverly Law, Hank Loescher, William Massman, Dayle McDermitt, William Munger, Andrew Suyker, Shashi Verma, Jon Welles and many others for their expertise in this area of flux studies We also thank Fluxnet, Canada Flux, and AmeriFlux networks for providing access to the data from the Eddy Covariance stations Burba & Anderson SlideSlide 5 5 ©LI-CORBurbaBiosciences & Anderson We also would like to thank numerous other researchers, technicians and students who, through years of use in the field, have developed the Eddy Covariance method to its present level and have proven its effectiveness with studies and scientific publications. 5 MAIN SEGMENTS I. Eddy Covariance Theory Overview II. Eddy Covariance Workflow III. Alternative Methods IV. Future Developments V. Summary VI. Useful Resources VII. References Burba & Anderson SlideSlide 6 6 ©LI-CORBurbaBiosciences & Anderson There are seven main parts to this guide: explanations of the basics of Eddy Covariance Theory; examples of Eddy Covariance Workflow; description of Alternative Flux Methods; discussion of Future Developments; Summary; and a list of Useful Resources and References. To by-pass chapters, you can use the clickable content of the outline on the left, and go to a specific chapter or slide. 6 I. EDDY COVARIANCE
Load more

Recommended publications
  • Measuring Evapotranspiration with LI-COR Eddy Flux Systems
    Measuring Evapotranspiration with LI-COR Eddy Flux Systems Application Note Change Log Introduction In situ, providing direct measurements of ET and sens- ible heat flux The largest flow of material in the biosphere is the move- Minimal disturbance to the region of interest ment of water through the hydrologic cycle (Chahine, 1992). The transfer of water from soil and water surfaces Measurements are spatially averaged over a large area through evaporation and the loss of water from plants Systems are automated for continuous long-term through stomata as transpiration represent the largest move- measurements ment of water to the atmosphere. Collectively these two pro- Instruments cesses are referred to as evapotranspiration (ET). The basic instruments required for eddy covariance meas- On a global scale, about 65% of land precipitation is urements include a water vapor analyzer and a sonic anem- returned to the atmosphere through evapotranspiration ometer (Figure 1), both of which must be capable of (Trenberth, 2007). Evapotranspiration is important to water making high frequency measurements. The H2O analyzer management, endangered species protection, and the genesis measures water vapor density, while the anemometer meas- of drought, flood, wildfire, and other natural disasters. In ures 3-dimensional wind speeds and directions. Meas- addition, ET, when thought of in terms of energy as latent urements are typically made at 10 Hz (10 times per second) heat flux, consumes about 50% of the solar radiation or faster in order to catch fast-moving eddies. absorbed by the earth’s surface (Trenberth, 2009). This influ- ences both climate and hydrology at local, regional, and global scales.
    [Show full text]
  • The Fundamental Equation of Eddy Covariance and Its Application in flux Measurementsଝ
    Agricultural and Forest Meteorology 152 (2012) 135–148 Contents lists available at SciVerse ScienceDirect Agricultural and Forest Meteorology jou rnal homepage: www.elsevier.com/locate/agrformet The fundamental equation of eddy covariance and its application in flux measurementsଝ a,∗ b c d e Lianhong Gu , William J. Massman , Ray Leuning , Stephen G. Pallardy , Tilden Meyers , a a d a Paul J. Hanson , Jeffery S. Riggs , Kevin P. Hosman , Bai Yang a Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA b USDA Forest Service, Rocky Mountain Research Station, 240 West Prospect, Fort Collins, CO 80526, USA c CSIRO Marine and Atmospheric Research, PO Box 3023, Canberra, ACT 2601, Australia d Department of Forestry, University of Missouri, Columbia, MO 65211, USA e Atmospheric Turbulence and Diffusion Division, Air Resources Laboratory, NOAA, Oak Ridge, TN 37830, USA a r t i c l e i n f o a b s t r a c t Article history: A fundamental equation of eddy covariance (FQEC) is derived that allows the net ecosystem exchange Received 18 May 2011 N (NEE) s of a specified atmospheric constituent s to be measured with the constraint of conservation Received in revised form N of any other atmospheric constituent (e.g. N2, argon, or dry air). It is shown that if the condition s 14 September 2011 s N Accepted 15 September 2011 CO2 is true, the conservation of mass can be applied with the assumption of no net ecosystem source or sink of dry air and the FQEC is reduced to the following equation and its approximation for horizontally Keywords: homogeneous mass fluxes: Fundamental equation of eddy covariance h h h WPL corrections ∂ ∂c ∂ N = c s d s w s d s + cd(z) dz + [s(z) − s(h)] dz ≈ cd(h) w s + dz .
    [Show full text]
  • Assessment and Simulation of Global Terrestrial Latent Heat Flux by Synthesis of CMIP5 Climate Models and Surface Eddy Covarianc
    Agricultural and Forest Meteorology 223 (2016) 151–167 Contents lists available at ScienceDirect Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations a,∗ a b a c Yunjun Yao , Shunlin Liang , Xianglan Li , Shaomin Liu , Jiquan Chen , a a a a d a a Xiaotong Zhang , Kun Jia , Bo Jiang , Xianhong Xie , Simon Munier , Meng Liu , Jian Yu e f g h i , Anders Lindroth , Andrej Varlagin , Antonio Raschi , Asko Noormets , Casimiro Pio , j,k l m,n o,p Georg Wohlfahrt , Ge Sun , Jean-Christophe Domec , Leonardo Montagnani , q m,n r s t Magnus Lund , Moors Eddy , Peter D. Blanken , Thomas Grünwald , Sebastian Wolf , u Vincenzo Magliulo a State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China b College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China c CGCEO/Geography, Michigan State University, East Lansing, MI 48824, USA d Laboratoire d’études en géophysique et océanographie spatiales, LEGOS/CNES/CN RS/IRD/UPS-UMR5566, Toulouse, France e GeoBiosphere Science Centre, Lund University, Sölvegatan 12, 223 62 Lund, Sweden f A.N.Severtsov Institute of Ecology and Evolution RAS 123103, Leninsky pr.33, Moscow, Russia g CNR-IBIMET, National Research Council, Via Caproni 8, 50145 Firenze, Italy h Dept. Forestry and Environmental Resources, North Carolina State University, 920 Main Campus Drive, Ste 300, Raleigh, NC 27695, USA i CESAM & Departamento de Ambiente e Ordenamento, Universidade de Aveiro, 3810-193 Aveiro, Portugal j Institute for Ecology, University of Innsbruck, Sternwartestr.
    [Show full text]
  • Dynamics of Canopy Stomatal Conductance, Transpiration, and Evaporation in a Temperate Deciduous Forest, Validated by Carbonyl S
    Biogeosciences Discuss., doi:10.5194/bg-2016-365, 2016 Manuscript under review for journal Biogeosciences Published: 2 September 2016 c Author(s) 2016. CC-BY 3.0 License. Dynamics of canopy stomatal conductance, transpiration, and evaporation in a temperate deciduous forest, validated by carbonyl sulfide uptake Richard Wehr1, Róisín Commane2, J. William Munger2, J. Barry McManus3, David D. Nelson3, Mark S. 3 1 2 5 Zahniser , Scott R. Saleska , Steven C. Wofsy 1Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, 85721, U.S.A. 2School of Engineering and Applied Sciences and Department of Earth and Planetary Sciences, Harvard University, Cambridge, 02138, U.S.A. 3Aerodyne Research Inc., Billerica, 01821, U.S.A. 10 Correspondence to: Richard Wehr ([email protected]) Abstract. Stomatal conductance influences both photosynthesis and transpiration, thereby coupling the carbon and water cycles and affecting surface-atmosphere energy exchange. The environmental response of stomatal conductance has been measured mainly at the leaf scale, and theoretical canopy models are relied on to upscale stomatal conductance for application in terrestrial ecosystem models and climate prediction. Here we estimate stomatal conductance and associated transpiration in a temperate 15 deciduous forest directly at the canopy scale via two independent approaches: (i) from heat and water vapor exchange, and (ii) from carbonyl sulfide (OCS) uptake. We use the eddy covariance method to measure the net ecosystem-atmosphere exchange of OCS, and we use a flux-gradient approach to separate canopy OCS uptake from soil OCS uptake. We find that the seasonal and diurnal patterns of canopy stomatal conductance obtained by the two approaches agree (to within ±6% diurnally), validating both methods.
    [Show full text]
  • A Review of Surface Energy Balance Models for Estimating Actual Evapotranspiration with Remote Sensing at High Spatiotemporal Resolution Over Large Extents
    Prepared in cooperation with the International Joint Commission A Review of Surface Energy Balance Models for Estimating Actual Evapotranspiration with Remote Sensing at High Spatiotemporal Resolution over Large Extents Scientific Investigations Report 2017–5087 U.S. Department of the Interior U.S. Geological Survey Cover. Aerial imagery of an irrigation district in southern California along the Colorado River with actual evapotranspiration modeled using Landsat data https://earthexplorer.usgs.gov; https://doi.org/10.5066/F7DF6PDR. A Review of Surface Energy Balance Models for Estimating Actual Evapotranspiration with Remote Sensing at High Spatiotemporal Resolution over Large Extents By Ryan R. McShane, Katelyn P. Driscoll, and Roy Sando Prepared in cooperation with the International Joint Commission Scientific Investigations Report 2017–5087 U.S. Department of the Interior U.S. Geological Survey U.S. Department of the Interior RYAN K. ZINKE, Secretary U.S. Geological Survey William H. Werkheiser, Acting Director U.S. Geological Survey, Reston, Virginia: 2017 For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment—visit https://www.usgs.gov or call 1–888–ASK–USGS. For an overview of USGS information products, including maps, imagery, and publications, visit https://store.usgs.gov. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.
    [Show full text]
  • Evapotranspiration Comparisons Between Eddy Covariance Measurements and Meteorological and Remote-Sensing-Based Models in Disturbed Ponderosa Pine Forests
    ECOHYDROLOGY Ecohydrol. (2014) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/eco.1586 Evapotranspiration comparisons between eddy covariance measurements and meteorological and remote-sensing-based models in disturbed ponderosa pine forests Wonsook Ha,1 Thomas E. Kolb,2,3 Abraham E. Springer,1* Sabina Dore,4 Frances C. O’Donnell,1 Rodolfo Martinez Morales,5 Sharon Masek Lopez1 and George W. Koch5,6 1 School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ, USA 2 School of Forestry, Northern Arizona University, Flagstaff, AZ, USA 3 Merriam-Powell Center for Environmental Research, Northern Arizona University, Flagstaff, AZ, USA 4 Department of Environmental Science, Policy, and Management, University of California at Berkeley, Berkeley, CA, USA 5 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA 6 Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA ABSTRACT Evapotranspiration (ET) comprises a major portion of the water budget in forests, yet few studies have measured or estimated ET in semi-arid, high-elevation ponderosa pine forests of the south-western USA or have investigated the capacity of models to predict ET in disturbed forests. We measured actual ET with the eddy covariance (eddy) method over 4 years in three ponderosa pine forests near Flagstaff, Arizona, that differ in disturbance history (undisturbed control, wildfire burned, and restoration À thinning) and compared these measurements (415–510 mm year 1 on average) with actual ET estimated from five meteorological models [Penman–Monteith (P-M), P-M with dynamic control of stomatal resistance (P-M-d), Priestley–Taylor (P-T), McNaughton–Black (M-B), and Shuttleworth–Wallace (S-W)] and from the Moderate Resolution Imaging Spectroradiometer (MODIS) ET product.
    [Show full text]
  • Modeling Gross Primary Production of Paddy Rice Cropland Through
    Remote Sensing of Environment 190 (2017) 42–55 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images Fengfei Xin a, Xiangming Xiao a,b,⁎,BinZhaoa, Akira Miyata c,DennisBaldocchid, Sara Knox d, Minseok Kang e, Kyo-moon Shim f, Sunghyun Min f,BangqianChena,g, Xiangping Li a, Jie Wang b, Jinwei Dong b,h, Chandrashekhar Biradar i a Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, Shanghai 200433, China b Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA c Division of Climate Change, Institute for Agro-Environmental Sciences, NARO, Tsukuba, 305-8604, Japan d Department of Environmental Science, Policy and management, University of California, Berkeley, CA 94720, USA e National Center for Agro Meteorology, Seoul 08826, South Korea f National Institute of Agricultural Sciences, Rural Development Administration, Wanju, 55365, South Korea g Rubber Research Institute (RRI), Chinese Academy of Tropical Agricultural Sciences(CATAS), Hainan Province, 571737, P. R. China h Institute of Geographical Science and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China i International Center for Agricultural Research in Dry Areas, Amman, 11195, Jordan article info abstract Article history: Accurate information on the gross primary production (GPP) of paddy rice cropland is critical for assessing and Received 1 April 2016 monitoring rice growing conditions. The eddy co-variance technique was used to measure net ecosystem ex- Received in revised form 28 October 2016 change (NEE) of CO2 between paddy rice croplands and the atmosphere, and the resultant NEE data then Accepted 30 November 2016 partitioned into GPP (GPP ) and ecosystem respiration.
    [Show full text]
  • LAI Influece on Bias in Night Time Eddy Covariance Data During Windy Conditions
    Proposal for a FLUXNET Synthesis paper Title: LAI influence on bias in night time eddy covariance data during windy conditions Outline: Many studies find that eddy covariance measurements are lower than chamber-based estimates although large uncertainties associated with scaling up often prevent distinction between measurements. Even in a very simple annual grass system where measurement precision was maximized, a distinction based on instantaneous data was often difficult. However, results from this system showed that with greater LAI, eddy covariance estimates became increasingly underestimated relative to chamber-based estimates during windy conditions at night (Myklebust, unpublished data). Using FLUXNET data, Misson et al. (2007) showed that within-canopy air movement at night was affected by overstory LAI in windy conditions. This suggests that above-canopy measurements of CO 2 flux may depend both on respiration and LAI. If this relationship is generally true across ecosystems, this bias has implications when comparing eddy flux estimates across ecosystems of dissimilar LAI. We would like to use the Fluxnet database to test the hypothesis that during windy conditions at night, the underestimate of eddy covariance measurements relative to chamber-based estimate means of ecosystem respiration increases with LAI. Variables required for this study include: eddy covariance and micrometeorological measurements, canopy profile measurements of CO 2, LAI, and chamber or model-based estimates of ecosystem respiration. Representatives from each site that we use in the analyses will be included as co-authors in publications resulting from this work. A potential complication of this relationship is that topography may influence advection in a site specific manner (Feigenwinter, et al., in press).
    [Show full text]
  • Error Characterization of Methane Fluxes and Budgets Derived from a Long-Term Comparison of Open- and Closed-Path Eddy Covarianc
    Agricultural and Forest Meteorology 278 (2019) 107638 Contents lists available at ScienceDirect Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet Error characterization of methane fluxes and budgets derived from a long- T term comparison of open- and closed-path eddy covariance systems ⁎ M. Julian Deventera, , Timothy J. Griffisa, D. Tyler Romanb, Randall K. Kolkab, Jeffrey D. Woodc, Matt Ericksona, John M. Bakera,d, Dylan B. Milleta a University of Minnesota – Dept. Soil, Water & Climate, United States b US Forest Service – Northern Research Station Grand Rapids, United States c University of Missouri – School of Natural Resources, United States d US Department of Agriculture – Agricultural Research Service, United States ARTICLE INFO ABSTRACT Keywords: Wetlands represent the dominant natural source of methane (CH4) to the atmosphere. Thus, substantial effort Eddy covariance has been spent examining the CH4 budgets of global wetlands via continuous ecosystem-scale measurements Methane using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, re- Uncertainty mains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting un- Error certainty in CH4 fluxes using a 3.5 year time series of simultaneous open- and closed4 pathCH flux measurements Budget over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH4 flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity.
    [Show full text]
  • Validation and Use of Satellite Remote Sensing Derived Evapotranspiration Estimates in Semi‐Arid Regions of South Africa
    VALIDATION AND USE OF SATELLITE REMOTE SENSING DERIVED EVAPOTRANSPIRATION ESTIMATES IN SEMI‐ARID REGIONS OF SOUTH AFRICA Nobuhle Patience Majozi VALIDATION AND USE OF SATELLITE REMOTE SENSING DERIVED EVAPOTRANSPIRATION ESTIMATES IN SEMI‐ ARID REGIONS OF SOUTH AFRICA DISSERTATION to obtain the degree of doctor at the University of Twente, on the authority of the rector magnificus, prof.dr. T.T.M. Palstra on account of the decision of the Doctorate Board, to be publicly defended on Friday 20 March 2020 at 14.45 hrs by Nobuhle Patience Majozi born on 29 March 1981 in Mount Frere, South Africa This dissertation has been approved by: Supervisors Prof.dr.ing. W. Verhoef A/Prof.dr.ir. C.M.M. Mannaerts Dissertation number 380 ISBN 978‐90‐365‐4984‐4 DOI 10.3990/1.9789036549844 Cover designed by Nobuhle Patience Majozi Printed by ITC printing department Copyright 2020 by Nobuhle Patience Majozi Graduation committee Chair Prof.dr.ir. A. Veldkamp Supervisors Prof.dr.ing. W. Verhoef A/Prof.dr.ir. C.M.M. Mannaerts Members Prof.dr. A.D. Nelson University of Twente Dr.ir. C. van der Tol University of Twente Prof.dr.ir. P. van der Zaag UN‐IHE & TU Delft Prof.dr.ir. Z. Bargaoui ENIT ‐ Universite El Manar, Tunis Dr. A.S. Ramoelo SanParks, Pretoria, SA Dr. R. Mathieu IRRI, Philippines In loving memory of my dearest mum, Agnes Mamase Hiyashe Summary Evapotranspiration is a key component of the hydrological cycle and has been identified as an Essential Climate Variable (ECV). It plays a critical role in the carbon‐energy‐water nexus, with latent heat flux being the largest heat sink in the atmosphere.
    [Show full text]
  • The Dynamics of Transpiration to Evapotranspiration Ratio Under Wet and Dry Canopy Conditions in a Humid Boreal Forest
    Article The Dynamics of Transpiration to Evapotranspiration Ratio under Wet and Dry Canopy Conditions in a Humid Boreal Forest Bram Hadiwijaya 1 , Steeve Pepin 2, Pierre-Erik Isabelle 1,3 and and Daniel F. Nadeau 1,* 1 CentrEau—Water Research Center, Department of Water and Civil Engineering, Université Laval, 1065 avenue de la Médecine, Québec, QC G1V 0A6, Canada; [email protected] (B.H.); [email protected] (P.-E.I.) 2 Centre de Recherche et d’Innovation sur les Végétaux, Department of Soil and Agri-Food Engineering, Université Laval, 2480 boulevard Hochelaga, Québec, QC G1V 0A6, Canada; [email protected] 3 Premier Tech, 1 avenue Premier, Rivière-du-Loup, QC G5R 6C1, Canada * Correspondence: [email protected] Received: 31 January 2020; Accepted: 19 February 2020; Published: 21 February 2020 Abstract: Humid boreal forests are unique environments characterized by a cold climate, abundant precipitation, and high evapotranspiration. Transpiration (ET), as a component of evapotranspiration (E), behaves differently under wet and dry canopy conditions, yet very few studies have focused on the dynamics of transpiration to evapotranspiration ratio (ET/E) under transient canopy wetness states. This study presents field measurements of ET/E at the Montmorency Forest, Québec, Canada: a balsam fir boreal forest that receives ∼ 1600 mm of precipitation annually (continental subarctic climate; Köppen classification subtype Dfc). Half-hourly observations of E and ET were obtained over two growing seasons using eddy-covariance and sap flow (Granier’s constant thermal dissipation) methods, respectively, under wet and dry canopy conditions. A series of calibration experiments were performed for sap flow, resulting in species-specific calibration coefficients that increased estimates of sap flux density by 34% ± 8%, compared to Granier’s original coefficients.
    [Show full text]
  • Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)
    remote sensing Article Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia) Koffi Dodji Noumonvi 1,* , Mitja Ferlan 1, Klemen Eler 2, Giorgio Alberti 3 , Alessandro Peressotti 3 and Sofia Cerasoli 4 1 Slovenian Forestry Institute, 1000 Ljubljana, Slovenia; [email protected] 2 Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia; [email protected] 3 Department of Agri-Food, Animal and Environmental Sciences, University of Udine, 33100 Udine, Italy; [email protected] (G.A.); [email protected] (A.P.) 4 Forest Research Center, University of Lisbon, 1349-017 Lisbon, Portugal; sofi[email protected] * Correspondence: koffi[email protected] Received: 19 January 2019; Accepted: 13 March 2019; Published: 16 March 2019 Abstract: The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE.
    [Show full text]